Below is results to our (preliminary) NHL Draft Model that uses prospects’ statistical production, physical measurements, and other variables to predict the likelihood that a players assume a specific NHL Role (i.e., First Line / Top Pair, Second / Third Line / 2nd pair defensemen, Fourth Line / Bottom pair defensemen, and Non-NHL player). The model is still being fine-tuned, hence the preliminary results, and an in-depth methodology article will come in the future. In addition to the aforementioned role probabilities, there is also a predicted NHL point per game that is derived from our Hockey Translation Factors.
If you didn’t know, there are a lot of people in the world—7.6 billion to be exact! There are also a lot of people that play hockey. As a result, there are a lot of hockey leagues in the world. Wow. Okay, moving on… The National Hockey League (NHL) is seen as the premier hockey league in the world, but players don’t start their hockey career in the NHL, and most never make it to the NHL. Some would argue that it is possible to have a successful and prosperous hockey career even if you never play in the NHL. In this article, I attempt to quantify the differences between these leagues; more specifically, translating individual player production from one league to the next. This would allow us to say things such as Tony Cameranesi registered 50 points in 50 games, or 1.0 point per game, in the NCAA, thus you would expect him to produce xx amount in the AHL, yy amount in the KHL, zz amount in the NHL, etc.
In the sport of hockey, we often value players that are more physical, especially those that additionally produce points. In this week’s My Model Monday, I explored the importance of hits on NHL hockey games. Continue reading My Model Monday: Understanding the Impact of Hits in the NHL
Below is a table of our 2017-18 NHL Playoff Simulation and Elo Ratings. Rankings are based on Probability of Winning the Conference Championships. Tune in to this post for updates to these figures throughout the rest of the 2017-18 NHL Playoffs.
With the NBA and NHL playoffs in full swing, it gives us a good chance to look at which teams over/underachieved during the regular season using Pythagorean Win Expectation, and, in turn, what teams could exceed expectations in the playoffs. Continue reading My Model Monday: NBA & NHL Pythagorean Wins
Below is our Model 284 consensus bracket for the 2017-18 NHL Playoffs as well as some Model Factoids. As you will see from our first round Predictions and playoff simulation results, we do not necessarily pick the model’s predicted winner for every single game, but use all available information (e.g. injuries, areas model might be lacking, etc.) to make the best prediction on each series.
In sports, people love to categorize players by their playing style. For example, in hockey, people distinguish defensemen as offensive or defensive, or the rare all-around defensemen. In this week’s installment of My Model Monday, I look to create mathematical groupings of NHL defensemen using 2017-2018 NHL data.
In this week’s My Model Monday, I compare our 2018 NHL Season Simulation results to the current NHL futures odds to see where our model is differing from betting markets. Continue reading My Model Monday: NHL Playoff Futures
In my first ever My Model Monday, I wanted to get back to my roots: ice hockey and St. Olaf College. For those who don’t know, I used to play ice hockey (sometimes) and did so at St. Olaf College; therefore, I figured it would be fun to bring some analysis to a sport and level that is rarely covered: Division III Men’s Ice Hockey.
Continue reading My Model Monday: DIII Men’s MIAC Hockey Rankings